Cited by 64Online and Blended LearningHigher Education Research StudiesEvasion and Academic Success Factors

Persistence and time challenges in an open online university: A case study of the experiences of first-year learners

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Xavier, Marlon, Meneses, Julio·2022·International Journal of Educational Technology in Higher Education

Abstract

Student persistence in the first year of studies is a crucial concern in online higher education. Recent accelerated growth in online programs due to the COVID pandemic has increased concerns over higher dropout rates, which are often connected to students’ time challenges—time poverty, juggling multiple commitments, and fitting studies into busy lives. However, research seldom focuses on students’ perceptions of time issues related to persistence. This study addresses this gap by exploring how 20 second-year students retrospectively viewed their experiences of time challenges and how they impacted their persistence in their first year at an online open university. Content analysis of in-depth interviews demonstrated that time pressure and time-conflicts were crucial barriers for success in the foundational semester; the main barrier was juggling study with multiple priorities. Most persisters had good time management and high levels of intrinsic motivation, satisfaction, and self-determination. However, even procrastinators with heavy work-family duties managed to persevere due to their resilience and personal motivation. Lastly, recommendations and strategies for effective student-based interventions to foster persistence are suggested.

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Research works printed but not linked: 26

  1. Ashby, A. (2004). Monitoring student retention in the Open University: definition, measurement, interpretation and action. Open Learning, 19(1), 65-77. https:// doi. org/ 10. 1080/ 02680 51042 00017 7854 · DOI
  2. Brown, M., Hughes, H., Keppell, M., Hard, N., & Smith, L. (2015). Stories from students in their first semester of distance learning. The International Review of Research in Open and Distributed Learning. https:// doi. org/ 10. 19173/ irrodl. v16i4. 1647 · DOI
  3. Carney-Crompton, S., & Tan, J. (2002). Support systems, psychological functioning, and academic performance of nontra- ditional female students. Adult Education Quarterly, 52(2), 140-154. https:// doi. org/ 10. 1177/ 07417 13602 05200 2005 · DOI
  4. Dews-Farrar, V. (2018). Students' reflections and experiences in online learning: a qualitative descriptive inquiry of persistence. [Doctoral dissertation]. Grand Canyon University. https:// search. proqu est. com/ docvi ew/ 20369 52458
  5. George, A., McEwan, A., & Tarr, J. (2021). Accountability in educational dialogue on attrition rates: understanding external attrition factors and isolation in online law school. Australasian Journal of Educational Technology, 37(1), 111-132. https:// doi. org/ 10. 14742/ ajet. 6175 · DOI
  6. Grau-Valldosera, J., Minguillon, J., & Blasco-Moreno, A. (2018). Returning after taking a break in online distance higher education: from intention to effective re-enrollment. Interactive Learning Environments, 27(3), 307-323. https:// doi. org/ 10. 1080/ 10494 820. 2018. 14709 86 · DOI
  7. Greenland, S., & Moore, C. (2021). Large qualitative sample and thematic analysis to redefine student dropout and reten- tion strategy in open online education. British Journal of Educational Technology. https:// doi. org/ 10. 1111/ bjet. 13173 · DOI
  8. Hart, C. (2012). Factors associated with student persistence in an online program of study: a review of the literature. Journal of Interactive Online Learning, 11(1), 19-42.
  9. Henry, M. (2018). The online student experience: an exploration of first-year university students' expectations, experiences and outcomes of online education. [Doctoral dissertation]. Edith Cowan University. https:// ro. ecu. edu. au/ theses/ 2059H older
  10. Kahu, E., Stephens, C., Zepke, N., & Leach, L. (2014). Space and time to engage: mature-aged distance students learn to fit study into their lives. International Journal of Lifelong Education, 33(4), 523-540. https:// doi. org/ 10. 1080/ 02601 370. 2014. 884177 · DOI
  11. Kember, D. (1989). A longitudinal-process model of drop-out from distance education. The Journal of Higher Education, 60(3), 278-301. https:// doi. org/ 10. 2307/ 19822 51 · DOI
  12. Korstange, R., Hall, J., Holcomb, J., & Jackson, J. (2020). The online first-year experience: defining and illustrating a new reality. Adult Learning. https:// doi. org/ 10. 1177/ 10451 59519 892680 · DOI
  13. Lee, K., Choi, H., & Cho, Y. (2019). Becoming a competent self: a developmental process of adult distance learning. Internet and Higher Education, 41, 25-33. https:// doi. org/ 10. 1016/j. iheduc. 2018. 12. 001 · DOI
  14. Lee, Y., & Choi, J. (2011). A review of online course dropout research: implications for practice and future research. Educa- tional Technology Research and Development, 59(5), 593-618. https:// doi. org/ 10. 1007/ s11423-010-9177-y · DOI · Paper page
  15. Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination, participation, and performance in online learning environments. Computers & Education, 56(1), 243-252. https:// doi. org/ 10. 1016/j. compe du. 2010. 07. 025 · DOI
  16. Myers, F., Glover, H., & Stephens, C. (2021). Learner interrupted: understanding the stories behind the codes-a qualitative analysis of HE distance-learner withdrawals. Journal of Further and Higher Education, 45(8), 1134-1146. https:// doi. org/ 10. 1080/ 03098 77X. 2021. 19310 61 · DOI
  17. Patton, M. (2015). Sampling, qualitative (purposeful). In G. Ritzer (Ed.), The Blackwell Encyclopedia of Sociology. John Wiley & sons. https:// doi. org/ 10. 1002/ 97814 05165 518. wbeos s012. pub2 · DOI
  18. Powell, R., Conway, C., & Ross, L. (1990). Effects of student predisposing characteristics on student success. International Journal of E-Learning and Distance Education, 5(1), 5-19.
  19. Rovai, A. (2003). In search of higher persistence rates in distance education online programs. Internet and Higher Educa- tion, 6(1), 1-16. https:// doi. org/ 10. 1016/ S1096-7516(02) 00158-6 · DOI · Paper page
  20. Sánchez-Gelabert, A., Valente, R., & Duart, J. (2020). Profiles of online students and the impact of their university experi- ence. International Review of Research in Open and Distributed Learning. https:// doi. org/ 10. 19173/ irrodl. v21i3. 4784 · DOI
  21. Stephen, J., Rockinson-Szapkiw, A., & Dubay, C. (2020). Persistence model of non-traditional online learners: self-efficacy, self-regulation, and self-direction. American Journal of Distance Education. https:// doi. org/ 10. 1080/ 08923 647. 2020. 17456 · DOI
  22. Thorpe, M. (2009). Perceptions about time and learning: Researching the student experience. In U. Bernath, A. Szücs, A. Tait, & M. Vidal (Eds.), Distance and e-learning in transition: learning innovation, technology and social challenges (pp. 457-472). ISTE.
  23. Tinto, V. (2017). Reflections on student persistence. Student Success, 8(2), 1-8. https:// doi. org/ 10. 5204/ ssj. v8i2. 376 · DOI
  24. Veletsianos, G., Kimmons, R., Larsen, R., & Rogers, J. (2021). Temporal flexibility, gender, and online learning completion. Distance Education, 42(1), 22-36. https:// doi. org/ 10. 1080/ 01587 919. 2020. 18695 · DOI
  25. Xavier, M., & Meneses, J. (2020). Dropout in online higher education: a scoping review from 2014 to 2018. Barcelona eLearn Centre/Universitat Oberta de Catalunya. https:// doi. org/ 10. 7238/ uoc. dropo ut. facto rs. 2020 · DOI
  26. Yin, R. K. (2003). Case study research: design and methods (3rd ed.). Sage Publishers.

Cited by

Sorted by citations

Authors
Xavier, M., & Meneses, J.
Affiliations
Universitat Oberta de Catalunya (ES)
Corresponding author
Xavier, Marlon
Venue
International Journal of Educational Technology in Higher Education
Published
2022-07-04
Volume
19
Issue
1
ISSN
2365-9440 (ISSN-L)
OpenAlex
W4283792516
Cited by
64
References
3
Citations by year
2026: 17 · 2025: 14 · 2024: 19 · 2023: 13 · 2022: 1
FWCI
25.35
Type
journal article
Language
en
Open access
Locations
  • International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • O2 - Repositori Institucional (Universitat Oberta de Catalunya) (submittedVersion, cc-by)
Topics
  • Social Sciences › Education › Online and Blended Learning
  • Social Sciences › Education › Higher Education Research Studies
  • Social Sciences › Demography › Evasion and Academic Success Factors
Keywords
student persistence, online higher education, first-year students, time poverty, time pressure, time conflict, procrastination, time management, intrinsic motivation, self-determination, student satisfaction, resilience, school dropout

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